OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing E-Learning System Through Learning Management System (LMS) Technologies: Reshape The Learner Experience
Venue: arXiv cs.CY 2023
Year: 2023

This paper aims to determine how the LMS Web portal application reshapes the learner experience through the developed E-Learning Management System using Data Mining Algorithm. The methodology that the researchers used is descriptive research involving the interpretation of the meaning or significance of what is described. Gather data from questionnaires, surveys, observations concerned with the study, and the chi-square formula for the statistical treatment of data. The findings of the study, the extent that LMS Web portal application reshapes the learner experience in terms of the following variables with the Average Weighted Mean (AWM): Flexible engagement of Learners in any device is highly satisfied; Personalize learning tracker is highly satisfied; Collaborating with the Learning Expert is highly satisfied; Provides user-friendly Teaching Tools is satisfied; Evident Learner Progress and Involvement and is satisfied. In the final analysis, this E-Learning System can fit any educational needs as follows: chat, virtual classes, supportive resources for the students, individual and group monitoring, and assessment using LMS as maximum efficiency. Moreover, this platform can be used to deliver hybrid learning.
Modeling Digital Twin Data and Architecture: A Building Guide with FIWARE as Enabling Technology
Venue: arXiv cs.CY 2023
Year: 2023

The use of Digital Twins in the industry has become a growing trend in recent years, allowing to improve the lifecycle of any process by taking advantage of the relationship between the physical and the virtual world. Existing literature formulates several challenges for building Digital Twins, as well as some proposals for overcoming them. However, in the vast majority of the cases, the architectures and technologies presented are strongly bounded to the domain where the Digital Twins are applied. This article proposes the FIWARE Ecosystem, combining its catalog of components and its Smart Data Models, as a solution for the development of any Digital Twin. We also provide a use case to showcase how to use FIWARE for building Digital Twins through a complete example of a Parking Digital Twin. We conclude that the FIWARE Ecosystem constitutes a real reference option for developing DTs in any domain.
ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case Reports
Venue: arXiv cs.CY 2023
Year: 2023

Objective: To evaluate the efficiency of large language models (LLMs) such as ChatGPT to assist in diagnosing neuro-ophthalmic diseases based on detailed case descriptions. Methods: We selected 22 different case reports of neuro-ophthalmic diseases from a publicly available online database. These cases included a wide range of chronic and acute diseases that are commonly seen by neuro-ophthalmic sub-specialists. We inserted the text from each case as a new prompt into both ChatGPT v3.5 and ChatGPT Plus v4.0 and asked for the most probable diagnosis. We then presented the exact information to two neuro-ophthalmologists and recorded their diagnoses followed by comparison to responses from both versions of ChatGPT. Results: ChatGPT v3.5, ChatGPT Plus v4.0, and the two neuro-ophthalmologists were correct in 13 (59%), 18 (82%), 19 (86%), and 19 (86%) out of 22 cases, respectively. The agreement between the various diagnostic sources were as follows: ChatGPT v3.5 and ChatGPT Plus v4.0, 13 (59%); ChatGPT v3.5 and the first neuro-ophthalmologist, 12 (55%); ChatGPT v3.5 and the second neuro-ophthalmologist, 12 (55%); ChatGPT Plus v4.0 and the first neuro-ophthalmologist, 17 (77%); ChatGPT Plus v4.0 and the second neuro-ophthalmologist, 16 (73%); and first and second neuro-ophthalmologists 17 (17%). Conclusions: The accuracy of ChatGPT v3.5 and ChatGPT Plus v4.0 in diagnosing patients with neuro-ophthalmic diseases was 59% and 82%, respectively. With further development, ChatGPT Plus v4.0 may have potential to be used in clinical care settings to assist clinicians in providing quick, accurate diagnoses of patients in neuro-ophthalmology. The applicability of using LLMs like ChatGPT in clinical settings that lack access to subspeciality trained neuro-ophthalmologists deserves further research.
Personalization, Cognition, and Gamification-based Programming Language Learning: A State-of-the-Art Systematic Literature Review
Venue: arXiv cs.CY 2023
Year: 2023

Programming courses in computing science are important because they are often the first introduction to computer programming for many students. Many university students are overwhelmed with the information they must learn for an introductory course. The current teacher-lecturer model of learning commonly employed in university lecture halls often results in a lack of motivation and participation in learning. Personalized gamification is a pedagogical approach that combines gamification and personalized learning to motivate and engage students while addressing individual differences in learning. This approach integrates gamification and personalized learning strategies to inspire and involve students while addressing their unique learning needs and differences. A comprehensive literature search was conducted by including 81 studies that were analyzed based on their research design, intervention, outcome measures, and quality assessment. The findings suggest that personalized gamification can enhance student cognition in programming courses by improving motivation, engagement, and learning outcomes. However, the effectiveness of personalized gamification varies depending on various factors, such as the type of gamification elements used, the degree of personalization, and the characteristics of the learners. This paper provides insights into designing and implementing effective personalized gamification interventions in programming courses. The findings could inform educational practitioners and researchers in programming education about the potential benefits of personalized gamification and its implications for educational practice.
Process Improvement Archaeology: What Led Us Here, and What's Next?
Venue: arXiv cs.SE 2023
Year: 2023

While in every organization corporate culture and history change over time, intentional efforts to identify performance problems are of particular interest when trying to understand the current state of an organization. The results of past improvement initiatives can shed light on the evolution of an organization and represent, with the advantage of perfect hindsight, a learning opportunity for future process improvements. The opportunity to test this premise occurred in an applied research collaboration with the Swedish Transport Administration, the government agency responsible for the planning, implementation, and maintenance of long-term rail, road, shipping, and aviation infrastructure in Sweden. This article is part of a theme issue on Process Improvement.
Studying and improving reasoning in humans and machines
Venue: arXiv cs.CL 2023
Year: 2023

In the present study, we investigate and compare reasoning in large language models (LLM) and humans using a selection of cognitive psychology tools traditionally dedicated to the study of (bounded) rationality. To do so, we presented to human participants and an array of pretrained LLMs new variants of classical cognitive experiments, and cross-compared their performances. Our results showed that most of the included models presented reasoning errors akin to those frequently ascribed to error-prone, heuristic-based human reasoning. Notwithstanding this superficial similarity, an in-depth comparison between humans and LLMs indicated important differences with human-like reasoning, with models limitations disappearing almost entirely in more recent LLMs releases. Moreover, we show that while it is possible to devise strategies to induce better performance, humans and machines are not equally-responsive to the same prompting schemes. We conclude by discussing the epistemological implications and challenges of comparing human and machine behavior for both artificial intelligence and cognitive psychology.
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Online local-life service platforms provide services like nearby daily essentials and food delivery for hundreds of millions of users. Different from other types of recommender systems, local-life service recommendation has the following characteristics: (1) spatiotemporal periodicity, which means a user's preferences for items vary from different locations at different times. (2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times. However, most existing methods either focus on merely the spatiotemporal contexts in sequences, or model the user-item interactions without spatiotemporal contexts in graphs. To address this issue, we design a new method named SPCS in this paper. Specifically, we propose a novel spatiotemporal graph transformer (SGT) layer, which explicitly encodes relative spatiotemporal contexts, and aggregates the information from multi-hop neighbors to unify spatiotemporal periodicity and collaborative signal. With extensive experiments on both public and industrial datasets, this paper validates the state-of-the-art performance of SPCS.
Learning to Diversify Neural Text Generation via Degenerative Model
Venue: arXiv cs.CL 2023
Year: 2023

Neural language models often fail to generate diverse and informative texts, limiting their applicability in real-world problems. While previous approaches have proposed to address these issues by identifying and penalizing undesirable behaviors (e.g., repetition, overuse of frequent words) from language models, we propose an alternative approach based on an observation: models primarily learn attributes within examples that are likely to cause degeneration problems. Based on this observation, we propose a new approach to prevent degeneration problems by training two models. Specifically, we first train a model that is designed to amplify undesirable patterns. We then enhance the diversity of the second model by focusing on patterns that the first model fails to learn. Extensive experiments on two tasks, namely language modeling and dialogue generation, demonstrate the effectiveness of our approach.
Quark: A High-Performance Secure Container Runtime for Serverless Computing
Venue: arXiv cs.NI 2023
Year: 2023

Secure container runtimes serve as the foundational layer for creating and running containers, which is the bedrock of emerging computing paradigms like microservices and serverless computing. Although existing secure container runtimes indeed enhance security via running containers over a guest kernel and a Virtual Machine Monitor (VMM or Hypervisor), they incur performance penalties in critical areas such as networking, container startup, and I/O system calls. In our practice of operating microservices and serverless computing, we build a high-performance secure container runtime named Quark. Unlike existing solutions that rely on traditional VM technologies by importing Linux for the guest kernel and QEMU for the VMM, we take a different approach to building Quark from the ground up, paving the way for extreme customization to unlock high performance. Our development centers on co-designing a custom guest kernel and a VMM for secure containers. To this end, we build a lightweight guest OS kernel named QKernel and a specialized VMM named QVisor. The QKernel-QVisor codesign allows us to deliver three key advancements: high-performance RDMA-based container networking, fast container startup mode, and efficient mechanisms for executing I/O syscalls. In our practice with real-world apps like Redis, Quark cuts down P95 latency by 79.3% and increases throughput by 2.43x compared to Kata. Moreover, Quark container startup achieves 96.5% lower latency than the cold-start mode while saving 81.3% memory cost to the keep-warm mode. Quark is open-source with an industry-standard codebase in Rust.
Learning Actions and Control of Focus of Attention with a Log-Polar-like Sensor
Venue: arXiv cs.RO 2023
Year: 2023

With the long-term goal of reducing the image processing time on an autonomous mobile robot in mind we explore in this paper the use of log-polar like image data with gaze control. The gaze control is not done on the Cartesian image but on the log-polar like image data. For this we start out from the classic deep reinforcement learning approach for Atari games. We extend an A3C deep RL approach with an LSTM network, and we learn the policy for playing three Atari games and a policy for gaze control. While the Atari games already use low-resolution images of 80 by 80 pixels, we are able to further reduce the amount of image pixels by a factor of 5 without losing any gaming performance.
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Surface defect inspection is of great importance for industrial manufacture and production. Though defect inspection methods based on deep learning have made significant progress, there are still some challenges for these methods, such as indistinguishable weak defects and defect-like interference in the background. To address these issues, we propose a transformer network with multi-stage CNN (Convolutional Neural Network) feature injection for surface defect segmentation, which is a UNet-like structure named CINFormer. CINFormer presents a simple yet effective feature integration mechanism that injects the multi-level CNN features of the input image into different stages of the transformer network in the encoder. This can maintain the merit of CNN capturing detailed features and that of transformer depressing noises in the background, which facilitates accurate defect detection. In addition, CINFormer presents a Top-K self-attention module to focus on tokens with more important information about the defects, so as to further reduce the impact of the redundant background. Extensive experiments conducted on the surface defect datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer achieves state-of-the-art performance in defect detection.
RHINO: Regularizing the Hash-based Implicit Neural Representation
Venue: arXiv cs.CV 2023
Year: 2023

The use of Implicit Neural Representation (INR) through a hash-table has demonstrated impressive effectiveness and efficiency in characterizing intricate signals. However, current state-of-the-art methods exhibit insufficient regularization, often yielding unreliable and noisy results during interpolations. We find that this issue stems from broken gradient flow between input coordinates and indexed hash-keys, where the chain rule attempts to model discrete hash-keys, rather than the continuous coordinates. To tackle this concern, we introduce RHINO, in which a continuous analytical function is incorporated to facilitate regularization by connecting the input coordinate and the network additionally without modifying the architecture of current hash-based INRs. This connection ensures a seamless backpropagation of gradients from the network's output back to the input coordinates, thereby enhancing regularization. Our experimental results not only showcase the broadened regularization capability across different hash-based INRs like DINER and Instant NGP, but also across a variety of tasks such as image fitting, representation of signed distance functions, and optimization of 5D static / 6D dynamic neural radiance fields. Notably, RHINO outperforms current state-of-the-art techniques in both quality and speed, affirming its superiority.
Truncated Laplace and Gaussian mechanisms of RDP
Venue: arXiv cs.CR 2023
Year: 2023

The Laplace mechanism and the Gaussian mechanism are primary mechanisms in differential privacy, widely applicable to many scenarios involving numerical data. However, due to the infinite-range random variables they generate, the Laplace and Gaussian mechanisms may return values that are semantically impossible, such as negative numbers. To address this issue, we have designed the truncated Laplace mechanism and Gaussian mechanism. For a given truncation interval [a, b], the truncated Gaussian mechanism ensures the same Renyi Differential Privacy (RDP) as the untruncated mechanism, regardless of the values chosen for the truncation interval [a, b]. Similarly, the truncated Laplace mechanism, for specified interval [a, b], maintains the same RDP as the untruncated mechanism. We provide the RDP expressions for each of them. We believe that our study can further enhance the utility of differential privacy in specific applications.
UWA360CAM: A 360$^{\circ}$ 24/7 Real-Time Streaming Camera System for Underwater Applications
Venue: arXiv cs.RO 2023
Year: 2023

Omnidirectional camera is a cost-effective and information-rich sensor highly suitable for many marine applications and the ocean scientific community, encompassing several domains such as augmented reality, mapping, motion estimation, visual surveillance, and simultaneous localization and mapping. However, designing and constructing such a high-quality 360$^{\circ}$ real-time streaming camera system for underwater applications is a challenging problem due to the technical complexity in several aspects including sensor resolution, wide field of view, power supply, optical design, system calibration, and overheating management. This paper presents a novel and comprehensive system that addresses the complexities associated with the design, construction, and implementation of a fully functional 360$^{\circ}$ real-time streaming camera system specifically tailored for underwater environments. Our proposed system, UWA360CAM, can stream video in real time, operate in 24/7, and capture 360$^{\circ}$ underwater panorama images. Notably, our work is the pioneering effort in providing a detailed and replicable account of this system. The experiments provide a comprehensive analysis of our proposed system.
HRoT: Hybrid prompt strategy and Retrieval of Thought for Table-Text Hybrid Question Answering
Venue: arXiv cs.CL 2023
Year: 2023

Answering numerical questions over hybrid contents from the given tables and text(TextTableQA) is a challenging task. Recently, Large Language Models (LLMs) have gained significant attention in the NLP community. With the emergence of large language models, In-Context Learning and Chain-of-Thought prompting have become two particularly popular research topics in this field. In this paper, we introduce a new prompting strategy called Hybrid prompt strategy and Retrieval of Thought for TextTableQA. Through In-Context Learning, we prompt the model to develop the ability of retrieval thinking when dealing with hybrid data. Our method achieves superior performance compared to the fully-supervised SOTA on the MultiHiertt dataset in the few-shot setting.
Enhancing Graph Representation of the Environment through Local and Cloud Computation
Venue: arXiv cs.RO 2023
Year: 2023

Enriching the robot representation of the operational environment is a challenging task that aims at bridging the gap between low-level sensor readings and high-level semantic understanding. Having a rich representation often requires computationally demanding architectures and pure point cloud based detection systems that struggle when dealing with everyday objects that have to be handled by the robot. To overcome these issues, we propose a graph-based representation that addresses this gap by providing a semantic representation of robot environments from multiple sources. In fact, to acquire information from the environment, the framework combines classical computer vision tools with modern computer vision cloud services, ensuring computational feasibility on onboard hardware. By incorporating an ontology hierarchy with over 800 object classes, the framework achieves cross-domain adaptability, eliminating the need for environment-specific tools. The proposed approach allows us to handle also small objects and integrate them into the semantic representation of the environment. The approach is implemented in the Robot Operating System (ROS) using the RViz visualizer for environment representation. This work is a first step towards the development of a general-purpose framework, to facilitate intuitive interaction and navigation across different domains.
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm
Venue: arXiv cs.LG 2023
Year: 2023

Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative. Motivated by noisy multi-class learning, the introduction of transition matrices can help model multi-label noise and enable the development of statistically consistent algorithms for noisy multi-label learning. However, estimating multi-label noise transition matrices remains a challenging task, as most existing estimators in noisy multi-class learning rely on anchor points and accurate fitting of noisy class posteriors, which is hard to satisfy in noisy multi-label learning. In this paper, we address this problem by first investigating the identifiability of class-dependent transition matrices in noisy multi-label learning. Building upon the identifiability results, we propose a novel estimator that leverages label correlations without the need for anchor points or precise fitting of noisy class posteriors. Specifically, we first estimate the occurrence probability of two noisy labels to capture noisy label correlations. Subsequently, we employ sample selection techniques to extract information implying clean label correlations, which are then used to estimate the occurrence probability of one noisy label when a certain clean label appears. By exploiting the mismatches in label correlations implied by these occurrence probabilities, we demonstrate that the transition matrix becomes identifiable and can be acquired by solving a bilinear decomposition problem. Theoretically, we establish an estimation error bound for our multi-label transition matrix estimator and derive a generalization error bound for our statistically consistent algorithm. Empirically, we validate the effectiveness of our estimator in estimating multi-label noise transition matrices, leading to excellent classification performance.
Do Repeat Yourself: Understanding Sufficient Conditions for Restricted Chase Non-Termination
Venue: arXiv cs.LO 2023
Year: 2023

The disjunctive restricted chase is a sound and complete procedure for solving boolean conjunctive query entailment over knowledge bases of disjunctive existential rules. Alas, this procedure does not always terminate and checking if it does is undecidable. However, we can use acyclicity notions (sufficient conditions that imply termination) to effectively apply the chase in many real-world cases. To know if these conditions are as general as possible, we can use cyclicity notions (sufficient conditions that imply non-termination). In this paper, we discuss some issues with previously existing cyclicity notions, propose some novel notions for non-termination by dismantling the original idea, and empirically verify the generality of the new criteria.